AI with robotics in surgery extreme environments

Abstract

Robotic surgery offers surgeons a greater degree of accuracy, versatility, and control than with standard techniques for other kinds of complicated procedures. The robotic surgery technology offers numerous advantages for patients and leads to unforeseen effects that are easier to predict when such a complex interactive device is used for treatment. The challenging complications that are occurred during robotic surgery include, risk of human error while operating the robotic system and the possibility for mechanical failure. The paper proposes Robot Assisted - Remote Center Surgical System (RA-RCSS) to improve mechanical malfunction threat and practical skills of surgeons through intra practice feedback and demonstration from human experts. A mask region-based supervised learning model is trained to conduct semantic segmentation of surgical instruments and targets to improve surgical coordinates further and to facilitate self-oriented practice. Furthermore, the master-slave bilateral technique is integrated with RA-RCSS to analyze the mechanical failures and malfunctions of the robotic system. The emerging safety standard environment is presented as a key enabling factor in the commercialization of autonomous surgical robots. The simulation analysis is performed based on accuracy, security, performance, and cost factor proves the reliability of the proposed framework.